Paper Info
Title
Differentiation of arterioles from venules in mouse histology images using machine learning.
Abstract
Analysis and morphological comparison of the arteriolar and venular components of a microvascular network are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained with smooth muscle alpha-actin. Classifiers trained on statistical and morphological features by supervised machine learning provided useful classification accuracy for differentiation of arterioles from venules, achieving an area under the receiver operating characteristic curve of 0.89. Feature selection was consistent across cross validation iterations, and a small set of two features was required to achieve the reported performance, suggesting the generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample and paves the way for high-throughput analysis of the arteriolar and venular networks in the mouse. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Year
DOI
Venue
2016
10.1117/1.JMI.4.2.021104
JOURNAL OF MEDICAL IMAGING
Keywords
Field
DocType
vasculature quantification,whole slide analysis,feature analysis,machine learning,arteriole venule classification
Generalizability theory,Computer vision,Receiver operating characteristic,Feature selection,Software system,Software,Artificial intelligence,Pattern recognition (psychology),Machine learning,Physics
Conference
Volume
Issue
ISSN
4
2
2329-4302
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
J. Sachi Elkerton100.34
Yiwen Xu200.34
j geoffrey pickering301.35
Aaron D Ward48922.61